Mapping Direct Seeded Rice in
Raichur District of Karnataka, India
Murail Krishna Gumma, Deepika Uppala, Irshad A. Mohammed, Anthony M. Whitbread, and Ismail Rafi Mohammed
Abstract
Across South Asia, the cost of rice cultivation has increased
due to labor shortage. Direct seeding of rice is widely pro-
moted in order to reduce labor demand during crop establish-
ment stage, and to benefit poor farmers. To facilitate planning
and to track farming practice changes, this study presents
techniques to spatially distinguish between direct seeded and
transplanted rice fields using multiple-sensor remote sensing
imagery. The District of Raichur, a major region in northeast
Karnataka, Central India, where irrigated rice is grown and
direct seeded rice has been widely promoted since 2000, was
selected as a case study. The extent of cropland was mapped
using Landsat-8, Moderate Resolution Imaging Spectroradi-
ometer (
MODIS
) 16-day normalized difference vegetation index
(
NDVI
) time-series data and the cultivation practice delineated
using
RISAT
-1 data for the year 2014. Areas grown to rice were
mapped based on the length of the growing period detected
using spectral characteristics and intensive field observa-
tions. The high resolution imagery of Landsat-8 was useful to
classify the rice growing areas. The accuracy of land-use/land-
cover (
LULC
) classes varied from 84 percent to 98 percent. The
results clearly demonstrated the usefulness of multiple-sensor
imagery from
MOD09Q1
, Landsat-8, and
RISAT
-1 in mapping the
rice area and practices accurately, routinely, and consistently.
The low cost of imagery backed by ground survey, as dem-
onstrated in this paper, can also be used across rice growing
countries to identify different rice systems.
Introduction
Agriculture is an important sector in India, contributing about
17.9 percent of the gross domestic product (
GDP
) (2014 figures)
(
CIA
, 2015). About 68 percent of the country’s population lives
in rural areas (
FAOSTAT
, 2013). India is one of the largest rice
growing nations accounting for one quarter of the global rice
area and produces around 125 million tons/year, with low
yields of around 2.85 t/ha (Siddiq, 2000). Given that rice is a
staple food crop in India and that the country has a popula-
tion that is growing at 1.2 percent per annum, a substantial
increase in rice production is the only way to guarantee food
security and to keep food prices low. Labor and fertilizer are
the two major input costs that are incurred while growing rice
when there is assured water availability (Savary
et al
., 2005).
Migration from rural to urban areas in search of work has led
to labor shortages and higher costs of agricultural operations
(Hossain, 1998; Savary
et al
., 2005). To overcome these con-
straints, promoting direct seeding in lieu of the traditional and
labor-intensive transplanting (De Datta, 1986; Naylor, 1992) is
being seen as a feasible alternative. However, there is a trad-
eoff with lower yields in rice that is direct seeded rather than
transplanted; so efforts are on to bridge this gap (Khush, 1995)
.
Accurate and timely information on rice systems, cropped
area, and yields are very important for sustainable food secu-
rity. Several studies have used remote sensing data to monitor
rice crop. Remote sensing technology provides a time saving
approach to estimate cropped area, intensity and other
LULC
changes in a country (Badhwar, 1984; Gumma
et al
., 2011a;
Gumma
et al
., 2015; Lobell
et al
., 2003; Thenkabail
et al
.,
2009a; Thenkabail, 2010; Thiruvengadachari and Sakthiva-
divel, 1997). Many studies have reported the use of spatial-
temporal data to map irrigated areas, land-use, land-cover,
and crop type (Dheeravath
et al
., 2010; Goetz
et al
., 2004;
Gumma
et al
., 2014; Knight
et al
., 2006; Thenkabail
et al
.,
2005; Varlyguin
et al
., 2001; Velpuri
et al
., 2009) using
MODIS
NDVI
time-series data to map both agricultural areas (Biggs
et
al
., 2006; Gaur
et al
., 2008; Gumma
et al
., 2011c) and seasonal
crop area (Sakamoto
et al
., 2005). There are also studies that
have used Synthetic Aperture Radar (
SAR
) data to monitor
rice areas, particularly irrigated rice. (Le Toan
et al
., 1997)
used
ERS-1
data as input to crop growth simulation models to
estimate production for study sites in Indonesia and Japan.
Similarly, (Shao
et al
., 2001) mapped rice areas using tempo-
ral
RADARSAT
data (1996 and 1997) for production estimates
in Zhaoqing in China. (Imhoff and Gesch, 1990) derived
sub-canopy digital terrain models of flooded forest using
SAR
.
(Kasischke and Bourgeau-Chavez, 1997) monitored the wet-
lands of South Florida using
ERS-1
SAR
imagery. (Leckie, 1990)
distinguished forest type using
SAR
and optical data. (Robin-
son
et al
., 2000) delineated drainage flow directions using
SAR
data. (Townsend, 2001) mapped seasonal flooding in forested
wetlands using temporal
RADARSAT
SAR
imagery. (Bouvet and
Le Toan, 2011) demonstrated how lower resolution wide-
swath images from advanced
SAR
(
ASAR
) data could be used to
map rice over larger areas. (Uppala
et al
., 2015) mapped rice
areas using single data hybrid polarimetric data from
RISAT-1
SAR
data. The advantage of rice mapping with
SAR
is that it
can overcome pervasive cloud cover across Asia during the
rainy season months when rice is cultivated. However, its
high cost inhibits its large-scale application
.
A comprehensive plan to increase agricultural productivity
and manage labor can be drawn up based on near real-time
monitoring of rice systems along with weather and crop data
acquired from an analysis of remote sensing imagery. Remote
sensing platforms have proved to be ideal to take resource
inventories and monitor agriculture. Interdisciplinary ap-
proaches and methods have made it easier to analyze imagery
and extract information in ways unknown a few decades ago
.
The goal of this study was to develop a method using mul-
tiple-sensor imagery (Landsat-8,
RISAT-1
and
MODIS
time-series
data) to map the spatial extent of two different rice cultiva-
tion practices (direct seeding and transplanting) in Raichur
district, Karnataka State, India.
International Crops Research Institute for the Semi-Arid
Tropics (ICRISAT), Patancheru, Telangana, India (m.gumma@
cgiar.org).
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 11, November 2015, pp. 873–880.
0099-1112/15/873–880
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.81.11.873
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
November 2015
873
